the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Mar 2025
Deep learning representation of the aerosol size distribution
Abstract. Aerosols influence Earth's radiative balance via the scattering and absorbing of solar radiation, affect cloud formation, and play important roles on precipitation, ocean seeding and human health. Accurate modeling of these effects requires knowledge of the the chemical composition and size distribution of aerosol particles present in the atmosphere. Computationally intensive applications like remote sensing and weather forecasting commonly use simplified representations of aerosol microphysics, prescribing the aerosol size distribution (ASD), introducing uncertainty in climate predictions and aerosol retrievals. This work develops a neural network model, termed MAMnet, to predict the ASD and mixing state using the bulk mass of aerosol and the meteorological state. MAMnet can be driven by the output of single moment, mass-based, aerosol schemes or using reanalysis products. We show that MAMnet is able to accurately reproduce the predictions of a two-moment microphysics aerosol model as well as field measurements. Our model paves the way to improve the physical representation of aerosols in physical models while maintaining the versatility and efficiency required in large scale applications.
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CEC1: 'Comment on egusphere-2025-482 - No compliance with the policy of the journal', Juan Antonio Añel, 07 Apr 2025
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
First, you have archived both the GEOS-ESM and the MAMnet code on GitHub. However, GitHub is not a suitable repository for scientific publication. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Therefore, the current situation with your manuscript is irregular. Please, publish your code in one of the appropriate repositories and reply to this comment with the relevant information (link and a permanent identifier for it (e.g. DOI)) as soon as possible, as we can not accept manuscripts in Discussions that do not comply with our policy.Also, in the Data Availability section of your manuscript you provide generic links to main web pages for the full datasets that provide access to the specific data that you have used in your work. We can not accept this. You must provide the exact data that you have used to develop your work. Importantly, in the case of the work that you present, the exact data used for the training of the neural network. This is critical to assure the replicability of your work, and therefore its scientific character.
I have to note that if you do not fix this problem, we will have to reject your manuscript for publication in our journal.
Finally, please, remember that you must include a modified 'Code and Data Availability' section in a potentially reviewed manuscript, containing the DOI of the new repositories that you create to solve the issues pointed out here.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/egusphere-2025-482-CEC1 -
RC1: 'Review on egusphere-2025-482', Anonymous Referee #1, 28 Apr 2025
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The manuscript describes the development of a neural-network model, MAMnet, trained on model output from GEOS+MAM, with the goal to create a computationally cheap platform to estimate aerosol size distributions using outputs from bulk aerosol models, with MERRA-2 used as an example. The work is interesting and is worthy of publication, after addressing my comments below, most of which are minor, but some might qualify as major ones, especially the evaluation part.
General comments
How does the trained model perform during a different time period? Aerosols in the 90s were much higher than they are today, is the model that is practically only driven by temperature (and air density, which does not change much with climate change) able to capture that time period? More generally, what is the validity range of the model, given its training dataset?
How much computational time is saved? There is no MERRA-2+MAM model, but the comparison between GEOS, GEOS+MAM, MERRA-2, and MERRA-2+MAMnet should be able to provide the necessary information.
I guess it is MAM7 used in this work; shouldn’t you be using this name to separate it from other MAM versions?
I am really surprised that only temperature and air density have been used for the meteorological state. I would expect that 3-dimensional wind fields (long-range transport), clouds and precipitation (wet removal, CCN, activation), and surface type (dry deposition) would be of key importance. Clouds can be also important for sulfate formation in the aqueous phase, and then cloud evaporation should affect sulfate size distribution. How can a model be accurate without these processes included?
The lifetime of a single species in MAM (e.g. SU) would depend by the removal rates in each mode, which differs in terms of mode solubility (a function of mode composition) and sedimentation velocity (a function of mode size). The NN training is implicitly using this information, but the NN application in a bulk model like GOCART does not have that distinction when calculating SU mass, so inherently SU is different across models by design. The NN will likely try to compensate that, but can you make a comment on this?
Specific comments
Line 9: Replace “physical representation” with “aerosol microphysics representation”. A machine-learned approach is not physics.
Line 24: “of the same size” should be “in the same bin”. Bulk approaches allow particles in different bins to have the same size but different composition, e.g. sulfate vs. nitrate.
Line 25: “they fail to distinguish” is too harsh, please replace with “they are not designed to resolve”. They would fail if they would try to resolve ASD, but they don’t.
Lines 38-39: “These models offer the most physically consistent representation of the ASD” is not necessarily correct, since modal models assume a shape of the size distribution per mode, typically a lognormal, which is an approximation of reality. One could argue that sectional models, which are even more expensive than modal ones, are better, since they can freely calculate the ASD shape without the need of a lognormal, but they also suffer from assumptions needed when moving mass and number from one section to another. Particle-resolved models might be the most realistic ones, but these are practically impossible to use in large-scale models. The point is that mentioning that modal schemes are the most physically consistent is incorrect.
Line 96: Which years were simulated, and 72 vertical levels up to what altitude?
Line 97: Please elaborate on the choice of 9 AM/PM UTC time for the output and especially the 12-hour frequency. Understandably this is a lot of output already, but I would argue that sampling any individual location just twice a day has a high probability to miss the diurnal variability of ASD. I would expect that 4 times a day would be the minimum reasonable sampling frequency, as a first guess.
Section 2.2.1: I do not follow the files counting and usage. 25 were “randomly selected without replacement for training” (what does that mean?), 10 were used “for the testing of the trained model”, 100 were “not used during training” (how were they used?). What are these files? Each instantaneous output produces one file, so 2 per day, times 365 times 5 years files? If yes, what happens with the remaining thousands of files? And how many have been used for training? I see later (lines 139-140) stated “5 output files for training, 2 for validation” which makes even less sense. Please explain.
Figure 1: Please explain what MAMnet loss is. It is not referenced anywhere else in the manuscript. Also, why GOCART is mentioned? This figure is for the development of the NN, not its application. Isn’t GOCART only used for application?
Table 3: Too many new concepts there which are not explained. Please help the reader understand what these are, or move this table in an appendix, if you consider it too technical to expand.
Section 3: I would recommend adding a section 3.1 “evaluation against GEOS+MAM”, similar as to what current section 3.1 says “evaluation against observations”, instead of having it under the generic section 3.
Figure 2: Are these global means per layer? Assuming that yes, is this a good metric, especially for number concentration? Wouldn’t doing this regionally be much more meaningful? I appreciate the zonal means and maps later, but my question stands. To be more specific, how can you say “systematic errors emerge” in line 199, without knowing whether this error is widespread or just some very large scattered errors that overwhelm the mean?
Figures 2-3, regarding mass concentrations: what is the model performance in terms of mass conservation? The results per mode do not need to conserve mass, but per species across modes mass conservation is paramount. Thinking even further, how will the mass conservation concept be applied when using MAMnet in production runs?
Lines 253-262, and Figure 6: These are an evaluation against MERRA-2, not observations, as the title of section 3.1 denotes. This whole paragraph and figure are a good conclusion in the discussion just before this section, so moving it right after line 247 and before section 3.1 starts should be considered.
Section 3.1: Although I agree with the motivational 1st paragraph of this section (lines 249-252), it sounds more than wishful thinking. MAMnet is trained with model data, not measurements, so at its peak performance it will be able to emulate the modeled data. In terms of measurements, it can only be as good as GEOS+MAM or MERRA-2 models, and any improvement in skill when compared with measurements (if at all evident) will be coincidental, thus irrelevant. What is really missing from both sections 3.1.1 and 3.1.2 is a baseline discussion: how does MERRA-2 alone perform when comparing with measurements? Of course MERRA-2 does not simulate ASD, but biases in the total aerosol mass (per species or not) will impact ASD. Even more, GEOS+MAM does not include assimilation, so other sorts of biases are likely present in the ASD of the training data set. Since this paper is about MAMnet, and since section 3.1 as a whole is to demonstrate its overall skill, not knowing the skill of the training dataset is a major shortcoming. To the very least, GEOS+MAM should be presented in figures 7 and 8, but a mass concentration comparison (or citation of past evaluation efforts) should be presented as well.
Section 3.2: please explain what Shapley values are exactly. There is some information in the figure legend, but a short introduction would be useful. Also, since this is a comparison against the model data, I would recommend moving it before the observations sections, so swapping sections 3.1 and 3.2.
Line 334: What do you mean by “possibly by promoting secondary aerosol formation” here? Secondary organics will evaporate more at higher temperatures, while secondary inorganic aerosols will have a more complex relationship depending on relative humidity as well.
Technical corrections
Line 44: Change “ML models, we can” to “ML models can”.
Line 79: Add “of different sizes” after “five mass bins”.
Line 80: Replace “hydrophilics” with “hydrophilic”.
Line 86: Table 2 is referenced before Table 1.
Line 97: Replace “these” with “that”.
Figure 1: rho_air is mentioned in the legend, but it is termed AIRD in the figure.
Line 109: Replace “Kg” with “kg”.
Lines 179 and 181: “the original MAM” and “GEOS+MAM” are the same thing, right? Please use one terminology throughout, for clarity.
Line 214: “smaller and less massive” is the same, why not just say “smaller”?
Line 217: Replace “near-perfect” with “very high”.
Line 223: Replace “sulfates” with “sulfate”.
Line 260: Replace “accurate” with “accurately”.
Line 311: Replace “tends align” with “tends to align”.
Figure 9: Please add a figure legend that explains the color lines, on top of the verbal description present in the caption.
Line 363: Replace “predicted concentrations” with “predicted number concentrations”.
Citation: https://doi.org/10.5194/egusphere-2025-482-RC1
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